Resumen
The article describes the role of high-performance computing (HPC) in solving challenges and problems in biology. Computer scientists and biomedical researchers face the challenge of transforming data into models and simulations that will enable scientists for the first time to gain a profound understanding of the deepest biological functions. Solving biological problems may require HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms but have massive computational requirements due to the large data sets that must be analyzed. Understanding evolution and the basic structure and function of proteins are two grand challenge problems in biology that can be solved only through the use of high-performance computing. Thus, the article investigates problems requiring massive parallelism due to inherent algorithmic complexity such as protein folding or due to being NP-hard such as inferring evolutionary histories from genetic information.